The ultimate goal of smart grid efforts for utility grids including electrical, water, and gas distribution networks is to enable continuous, real-time, automated optimization of grid conditions to promote goals such as improving efficiency, integrating renwable sources into generation, localizing and characterizing faults, routing utilities around faults to reduce risk and losses, effectively dispatching limited maintenance personnel and resources to potential grid pathologies and other such goals. However, these efforts have been constrained by the grid intelligence possible through current data aggregation and analysis schemes. These schemes take time to produce grid intelligence which is based on correlations, a level of knowledge that may be insufficient to fully achieve automated, real-time optimization pursuing multiple grid goals, and may struggle to identify relationships between particular grid actions or events and any temporally and/or spatially distant effects of those actions or events.
Current big-data modeling approaches to grid intelligence also yield conclusions that may not be readily actionable given the currently existing points of control over the grid, and that are based only on correlations, which include uncertainty that is not precisely computable and results from potential third variables driving observed relationships, and uncertainty about the directionality of those relationships. This uncertainty frequently requires expert humans in the loop to further interpret the observed relationships to develop plans of action, precluding real-time optimization. By capturing and processing the data separate from control of the grid operations, current approaches can achieve only abstract understandings of the links between grid controls and optimal grid conditions. Active machine learning techniques lack perfect experimental controls, remaining susceptible to uncertainty arising from third variable and directionality problems.
Real-time multi-objective optimization requires current, causal knowledge about the specific effects of control decisions, in order to allow for the inherent trade-offs in utility grid operations to be made appropriately. There is a need for the ability to generate control-centered causal knowledge of the effects of controls and latent independent variables affecting the grid, and automatically, continuously, and in real-time, apply that knowledge to driving desirable grid conditions and promoting safety and efficiency while detecting and mitigating grid faults.